Variable selection for generalized canonical correlation analysis.
Identifieur interne : 003375 ( Main/Exploration ); précédent : 003374; suivant : 003376Variable selection for generalized canonical correlation analysis.
Auteurs : Arthur Tenenhaus [France] ; Cathy Philippe [France] ; Vincent Guillemot [France] ; Kim-Anh Le Cao [Australie] ; Jacques Grill [France] ; Vincent Frouin [France]Source :
- Biostatistics (Oxford, England) [ 1468-4357 ] ; 2014.
Descripteurs français
- KwdFr :
- MESH :
- épidémiologie : Tumeurs du cerveau.
- Enfant, Humains, Interprétation statistique de données, Modèles statistiques, Simulation numérique.
English descriptors
- KwdEn :
- MESH :
- epidemiology : Brain Neoplasms.
- Child, Computer Simulation, Data Interpretation, Statistical, Humans, Models, Statistical.
Abstract
Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to 3 or more sets of variables. RGCCA is a component-based approach which aims to study the relationships between several sets of variables. The quality and interpretability of the RGCCA components are likely to be affected by the usefulness and relevance of the variables in each block. Therefore, it is an important issue to identify within each block which subsets of significant variables are active in the relationships between blocks. In this paper, RGCCA is extended to address the issue of variable selection. Specifically, sparse generalized canonical correlation analysis (SGCCA) is proposed to combine RGCCA with an [Formula: see text]-penalty in a unified framework. Within this framework, blocks are not necessarily fully connected, which makes SGCCA a flexible method for analyzing a wide variety of practical problems. Finally, the versatility and usefulness of SGCCA are illustrated on a simulated dataset and on a 3-block dataset which combine gene expression, comparative genomic hybridization, and a qualitative phenotype measured on a set of 53 children with glioma. SGCCA is available on CRAN as part of the RGCCA package.
DOI: 10.1093/biostatistics/kxu001
PubMed: 24550197
Affiliations:
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4072</wicri:noRegion></affiliation></author><author><name sortKey="Grill, Jacques" sort="Grill, Jacques" uniqKey="Grill J" first="Jacques" last="Grill">Jacques Grill</name><affiliation wicri:level="1"><nlm:affiliation>CNRS-IGR-Paris XI university, UMR8203, 94805 Villejuif cedex, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>CNRS-IGR-Paris XI university, UMR8203, 94805 Villejuif cedex</wicri:regionArea><wicri:noRegion>94805 Villejuif cedex</wicri:noRegion><wicri:noRegion>94805 Villejuif cedex</wicri:noRegion></affiliation></author><author><name sortKey="Frouin, Vincent" sort="Frouin, Vincent" uniqKey="Frouin V" first="Vincent" last="Frouin">Vincent Frouin</name><affiliation wicri:level="1"><nlm:affiliation>NEUROSPIN, I2BM, CEA saclay, 91191 Gif-sur-Yvette cedex, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>NEUROSPIN, I2BM, CEA saclay, 91191 Gif-sur-Yvette cedex</wicri:regionArea><wicri:noRegion>91191 Gif-sur-Yvette cedex</wicri:noRegion><wicri:noRegion>91191 Gif-sur-Yvette cedex</wicri:noRegion></affiliation></author></analytic><series><title level="j">Biostatistics (Oxford, England)</title><idno type="eISSN">1468-4357</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Brain Neoplasms (epidemiology)</term><term>Child</term><term>Computer Simulation</term><term>Data Interpretation, Statistical</term><term>Humans</term><term>Models, Statistical</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Enfant</term><term>Humains</term><term>Interprétation statistique de données</term><term>Modèles statistiques</term><term>Simulation numérique</term><term>Tumeurs du cerveau (épidémiologie)</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Brain Neoplasms</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Tumeurs du cerveau</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Child</term><term>Computer Simulation</term><term>Data Interpretation, Statistical</term><term>Humans</term><term>Models, Statistical</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Enfant</term><term>Humains</term><term>Interprétation statistique de données</term><term>Modèles statistiques</term><term>Simulation numérique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to 3 or more sets of variables. RGCCA is a component-based approach which aims to study the relationships between several sets of variables. The quality and interpretability of the RGCCA components are likely to be affected by the usefulness and relevance of the variables in each block. Therefore, it is an important issue to identify within each block which subsets of significant variables are active in the relationships between blocks. In this paper, RGCCA is extended to address the issue of variable selection. Specifically, sparse generalized canonical correlation analysis (SGCCA) is proposed to combine RGCCA with an [Formula: see text]-penalty in a unified framework. Within this framework, blocks are not necessarily fully connected, which makes SGCCA a flexible method for analyzing a wide variety of practical problems. Finally, the versatility and usefulness of SGCCA are illustrated on a simulated dataset and on a 3-block dataset which combine gene expression, comparative genomic hybridization, and a qualitative phenotype measured on a set of 53 children with glioma. SGCCA is available on CRAN as part of the RGCCA package.</div></front></TEI><affiliations><list><country><li>Australie</li><li>France</li></country><region><li>Île-de-France</li></region><settlement><li>Gif-sur-Yvette</li><li>Villejuif</li></settlement></list><tree><country name="France"><region name="Île-de-France"><name sortKey="Tenenhaus, Arthur" sort="Tenenhaus, Arthur" uniqKey="Tenenhaus A" first="Arthur" last="Tenenhaus">Arthur Tenenhaus</name></region><name sortKey="Frouin, Vincent" sort="Frouin, Vincent" uniqKey="Frouin V" first="Vincent" last="Frouin">Vincent Frouin</name><name sortKey="Grill, Jacques" sort="Grill, Jacques" uniqKey="Grill J" first="Jacques" last="Grill">Jacques Grill</name><name sortKey="Guillemot, Vincent" sort="Guillemot, Vincent" uniqKey="Guillemot V" first="Vincent" last="Guillemot">Vincent Guillemot</name><name sortKey="Philippe, Cathy" sort="Philippe, Cathy" uniqKey="Philippe C" first="Cathy" last="Philippe">Cathy Philippe</name></country><country name="Australie"><noRegion><name sortKey="Le Cao, Kim Anh" sort="Le Cao, Kim Anh" uniqKey="Le Cao K" first="Kim-Anh" last="Le Cao">Kim-Anh Le Cao</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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